Reading aloud: On the determinants of the joint effects of stimulus quality and word frequency.
Bibliographic record
Abstract
There are multiple reports, in the context of the time taken to read aloud, that the joint effects of stimulus quality and word frequency (a) interact when only words appear in the list but (b) are additive when nonwords are intermixed with words (O'Malley & Besner, 2008). This triple interaction has been explained in terms of the idea that different processing modes are in play in these different contexts. Processing is cascaded when only words appear in the list, allowing the effect of stimulus quality to influence the downstream process(es) affected by word frequency. In contrast, when nonwords appear in the list an early process affected by stimulus quality, but not word frequency, is staged (thresholded) so as to reduce the probability of lexicalizations (reading a nonword as a word) when stimulus quality is low. The present experiment addresses the issue of whether such thresholding in the presence of nonwords is driven by the orthography or phonology of the nonwords included in the stimulus set. Participants read words aloud that varied in word frequency and were randomly intermixed with nonwords that all sounded identical to words (e.g., BRANE for BRAIN). Stimulus quality and word frequency had additive effects on the time to read aloud in this context, consistent with the view that it is the orthography of the nonwords that matters. Other aspects of the results suggest that between level feed-back is in play when this particular kind of nonword is used. (PsycINFO Database Record
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".